This tutorial demonstrates
how to use Principal Component Analysis as a method of extracting more
information from data. The tutorial covers data import and displaying
PCA results in various plots including: scree, loadings line, color matrix,
score (raw and normalized) and 3D score (raw and normalized) plots.

This workflow is used
for ratio (Cy3/Cy5) data to filter out genes that do not show a large
induction or repression in any sample in the dataset, and then to log
normalize the data so that inductions and repressions have equal but opposite
sign.

This tutorial demonstrates
how to train GeneLinkerô Platinum's artificial neural networks ANNs) to
distinguish between sample classes. As an example, data on four similar
tumor types is studied. Program features covered include importing variables,
the SLAMô association-mining technology (algorithm and viewer), creating
gene lists for filtering, filtering, classification, and classification
plots.

This tutorial demonstrates
how to search for a gene to use as an IBIS classifier. One IBIS classifier
is produced using Linear Discriminant Analysis (LDA) and a second is produced
using Quadratic Discriminant Analysis (QDA). An IBIS Gradient plot is
used to analyze the results of the classifier creation.

This tutorial demonstrates
how to train GeneLinkerô Platinum's committee of SVMs to
distinguish between sample classes. As an example, data on two leukemia types is studied. Program features covered include importing variables, creating learners,
classification, and classification
plots.